SIB Days 2020 - Virtual Conference

Introduction

Welcome to the SIB Days 2020 - virtual conference Spatial Transcriptomics workshop by 10x genomics!

The purpose of this tutorial will be to walk users through some of the steps necessary to explore data produced by the 10x Genomics Visium Spatail Gene Expression Solution and the Spaceranger pipeline. We will investigate the datasets whith are all freely available from 10x Genomics.

Seurat Tutorial

Things to know about this workshop

1. All files that will be used can be found at: /mnt/libs/shared_data/
2. Getting started with R and Visium data outside of Seurat at: https://support.10xgenomics.com/spatial-gene-expression/software/pipelines/latest/rkit
3. Reference genome for all samples is GRCh38/mm10
4. All 10x software including Spaceranger, and Loupe Browser, can be downloaded from the 10x Support Site

Exploring Visium Data with Seurat

Load our packages

library(Seurat)
library(ggplot2)
library(patchwork)
library(dplyr)

Loading data in a Seurat object

Real Dataset for the tutorial

mouse_brain_sa <- Load10X_Spatial(data.dir = "/mnt/libs/shared_data/mouse_brain_sa/outs/",
                filename = "V1_Mouse_Brain_Sagittal_Anterior_filtered_feature_bc_matrix.h5")

Same data just internal to 10x

mouse_brain_sa <- Load10X_Spatial(data.dir = "/mnt/analysis/marsoc/pipestances/HMKLFDMXX/SPATIAL_RNA_COUNTER_PD/160121/HEAD/outs/")

There are a bunch of datasets hoted by the Satija lab in the Seurat Data Package.

Results

QC

Let’s have a look at some basic QC information. Keep in mind that most seurat plots are ggplot object and can be manipulated as such.

Counts = UMI Features = Genes

plot1 <- VlnPlot(mouse_brain_sa, features = "nCount_Spatial", pt.size = 0.1) + 
  ggtitle("UMI") +
  theme(axis.text.x = element_blank(), axis.title.x = element_blank(), legend.position = "right") +
  NoLegend()
plot2 <- VlnPlot(mouse_brain_sa, features = "nFeature_Spatial", pt.size = 0.1) + 
  ggtitle("Genes") +
  theme(axis.text.x = element_blank(), axis.title.x = element_blank(), legend.position = "right") +
  NoLegend()
plot3 <- SpatialFeaturePlot(mouse_brain_sa, features = "nCount_Spatial") + 
  theme(legend.position = "right")
plot4 <- SpatialFeaturePlot(mouse_brain_sa, features = "nFeature_Spatial") +
  theme(legend.position = "right")

plot1 + plot2 + plot3 + plot4 + plot_layout(nrow = 2, ncol = 2)

Normilization

Spaceranger does normiliaztion for clustering and DE but does not return that normalized matrix

Pre-normalization Raw UMI counts

SE transform

Don’t worry about reachediteration limit warnings

Default assay will now be set to SCT

mouse_brain_sa <- SCTransform(mouse_brain_sa, assay = "Spatial", verbose = TRUE)

From Seurat:

The default parameters in Seurat emphasize the visualization of molecular data. However, you can also adjust the size of the spots (and their transparency) to improve the visualization of the histology image, by changing the following parameters:

pt.size.factor- This will scale the size of the spots. Default is 1.6
alpha - minimum and maximum transparency. Default is c(1, 1).
Try setting to alpha c(0.1, 1), to downweight the transparency of points with lower expression

p1 <- SpatialFeaturePlot(mouse_brain_sa, features = "Ttr", pt.size.factor = 1)+ 
  theme(legend.position = "right") +
  ggtitle("Actual Spot Size")
p2 <- SpatialFeaturePlot(mouse_brain_sa, features = "Ttr", alpha = c(0.1, 1))+ 
  theme(legend.position = "right") +
  ggtitle("Scaled Spot Size")
p1 + p2

Dimensionality reduction, clustering, and visualization

We can then proceed to run dimensionality reduction and clustering on the RNA expression data, using the same workflow as we use for scRNA-seq analysis.

Some of these processes can be parallized

library(future)
# check the current active plan
plan()

sequential:
- args: function (expr, envir = parent.frame(), substitute = TRUE, lazy = FALSE, seed = NULL, globals = TRUE, local = TRUE, earlySignal = FALSE, label = NULL, ...)
- tweaked: FALSE
- call: NULL

# change the current plan to access parallelization
plan("multiprocess", workers = 4)

[ONE-TIME WARNING] Forked processing ('multicore') is disabled in future (>= 1.13.0) when running R from RStudio, because it is considered unstable. Because of this, plan("multicore") will fall back to plan("sequential"), and plan("multiprocess") will fall back to plan("multisession") - not plan("multicore") as in the past. For more details, how to control forked processing or not, and how to silence this warning in future R sessions, see ?future::supportsMulticore

plan()

multiprocess:
- args: function (expr, envir = parent.frame(), substitute = TRUE, lazy = FALSE, seed = NULL, globals = TRUE, workers = 4, gc = FALSE, earlySignal = FALSE, label = NULL, ...)
- tweaked: TRUE
- call: plan("multiprocess", workers = 4)

mouse_brain_sa <- RunPCA(mouse_brain_sa, assay = "SCT", verbose = FALSE)
mouse_brain_sa <- FindNeighbors(mouse_brain_sa, reduction = "pca", dims = 1:30)

Computing nearest neighbor graph
Computing SNN

mouse_brain_sa <- FindClusters(mouse_brain_sa, verbose = FALSE)

mouse_brain_sa <- RunUMAP(mouse_brain_sa, reduction = "pca", dims = 1:30)

The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session15:46:04 UMAP embedding parameters a = 0.9922 b = 1.112
15:46:04 Read 2696 rows and found 30 numeric columns
15:46:04 Using Annoy for neighbor search, n_neighbors = 30
15:46:04 Building Annoy index with metric = cosine, n_trees = 50
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:46:04 Writing NN index file to temp file /tmp/RtmpdiyDPO/file86f2c25794501
15:46:04 Searching Annoy index using 4 threads, search_k = 3000
15:46:04 Annoy recall = 100%
15:46:05 Commencing smooth kNN distance calibration using 4 threads
15:46:06 Initializing from normalized Laplacian + noise
15:46:08 Commencing optimization for 500 epochs, with 105850 positive edges
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
15:46:15 Optimization finished

Now let’s have a look at the clustering

I don’t really like these colors so let’s change them

p1 <- DimPlot(mouse_brain_sa, reduction = "umap", label = TRUE) +
  labs(color = "Cluster")
p2 <- SpatialDimPlot(mouse_brain_sa, label = TRUE, label.size = 3) +
  labs(fill = "Cluster")
p1 + p2

myPalette <- colorRampPalette(rev(brewer.pal(11, "Spectral")))

Error in brewer.pal(11, "Spectral") : 
  could not find function "brewer.pal"

Interactivity not working for me on firefox

Need to upgrade my seurat

SpatialFeaturePlot(mouse_brain_sa, features = "Ttr", do.hover = TRUE)

LinkedDimPlot(mouse_brain_sa)

Spatially variable features

First we’ll idetify differentially expressed genes.

Parallelization helps here too let’s make sure our plan is still intact

plan()

multiprocess:
- args: function (expr, envir = parent.frame(), substitute = TRUE, lazy = FALSE, seed = NULL, globals = TRUE, workers = 4, gc = FALSE, earlySignal = FALSE, label = NULL, ...)
- tweaked: TRUE
- call: plan("multiprocess", workers = 4)

- call: plan("multiprocess", workers = 4) indicates that it is

Looks like we have some very DE genes for clusters 4 and 11

clarify what ident.1 = 4, ident.2 = 6 are for

de_markers <- FindMarkers(mouse_brain_sa, ident.1 = 4, ident.2 = 6)

what are the top variable features?

VariableFeatures(mouse_brain_sa)[1:10]

 [1] "Ttr"     "Plp1"    "Hba-a1"  "Hbb-bs"  "Mbp"     "Penk"    "Ptgds"   "Hba-a2"  "S100a5"  "Ppp1r1b"

what are the top de genes?

rownames(de_markers)[1:10]

 [1] "Calb2"   "Slc6a11" "Ckb"     "Camk2n1" "Doc2g"   "Camk2a"  "Th"      "Nrgn"    "Cdhr1"   "Slc1a2" 

Using the top 100 variable genes find spatially enriched ones This will take X minutes something isn’t right here taking way too long

mouse_brain_sa <- FindSpatiallyVariableFeatures(mouse_brain_sa, assay = "SCT", features = VariableFeatures(mouse_brain_sa)[1:100], 
    selection.method = "markvariogram", verbose = TRUE)

images_cl <- list()

for (i in 1:length(lenas)) {
  images_cl[[i]] <- readbitmap::read.bitmap(paste(get_spatial_path(lenas[i]), "/outs/spatial/tissue_lowres_image.png", sep = ""))
}

height <- list()

for (i in 1:length(lenas)) {
 height[[i]] <-  data.frame(height = nrow(images_cl[[i]]))
}

height <- bind_rows(height)

width <- list()

for (i in 1:length(lenas)) {
 width[[i]] <- data.frame(width = ncol(images_cl[[i]]))
}

width <- bind_rows(width)

###Color from pipeline

grobs <- list()

for (i in 1:length(lenas)) {
  grobs[[i]] <- grid::rasterGrob(images_cl[[i]], width=unit(1,"npc"), height=unit(1,"npc"))
}

images_tibble <- tibble(lena=lenas, grob=grobs)
images_tibble$lena <- factor(images_tibble$lena)
images_tibble$height <- height$height
images_tibble$width <- width$width
images_tibble

scales <- list()

for (i in 1:length(lenas)) {
 path_scales <- paste(svenLib::get_spatial_path(lenas[i]), "/outs/spatial/scalefactors_json.json", sep = "")
 scales[[i]] <- rjson::fromJSON(file = path_scales)
}

Clusters and tsne

clusters <- list()
for (i in 1:length(lenas)) {
  clusters[[i]] <- read.csv(paste(svenLib::get_spatial_path(lenas[i]),"/outs/analysis_csv/clustering/graphclust/clusters.csv", sep = ""))
}


tsne <- list()

for (i in 1:length(lenas)) {
   tsne[[i]] <- read.csv(paste(svenLib::get_spatial_path(lenas[i]),"/outs/analysis_csv/tsne/2_components/projection.csv",sep = ""), header = T)
}

umap <- list()

for (i in 1:length(lenas)) {
   umap[[i]] <- read.csv(paste(svenLib::get_spatial_path(lenas[i]),"/outs/analysis_csv/umap/2_components/projection.csv",sep = ""), header = T)
}

umap[[1]]

Combine clusters, tsne, and tissue info for easy plotting

bcs <- list()

for (i in 1:length(lenas)) {
   if (file.exists(paste(get_spatial_path(lenas[i]),"/outs/spatial/tissue_positions_list.txt", sep = ""))) {
  bcs[[i]] <- read.csv(paste(get_spatial_path(lenas[i]), "/outs/spatial/tissue_positions_list.txt", sep = ""),
                 col.names=c("barcode","tissue","row","col","imagerow","imagecol"), header = F)
        } else {
  bcs[[i]] <- read.csv(paste(get_spatial_path(lenas[i]), "/outs/spatial/tissue_positions_list.csv", sep = ""),
                 col.names=c( "barcode","tissue","row","col","imagerow","imagecol"), header = F)
        }
   bcs[[i]]$imagerow_scaled <- bcs[[i]]$imagerow * scales[[i]]$tissue_lowres_scalef    # scale tissue coordinates for lowres image
   bcs[[i]]$imagecol_scaled <- bcs[[i]]$imagecol * scales[[i]]$tissue_lowres_scalef
   bcs[[i]]$imagerow_scaled_round <- round(bcs[[i]]$imagerow * scales[[i]]$tissue_lowres_scalef) # Rounded scales
   bcs[[i]]$imagecol_scaled_round <- round(bcs[[i]]$imagecol * scales[[i]]$tissue_lowres_scalef)
   bcs[[i]]$tissue <- as.factor(bcs[[i]]$tissue)
   bcs[[i]] <- merge(bcs[[i]], clusters[[i]], by.x = "barcode", by.y = "Barcode", all = TRUE)
   bcs[[i]] <- merge(bcs[[i]], tsne[[i]], by.x = "barcode", by.y = "Barcode", all = TRUE)
   bcs[[i]] <- merge(bcs[[i]], umap[[i]], by.x = "barcode", by.y = "Barcode", all = TRUE)
   bcs[[i]]$height <- height$height[i]
   bcs[[i]]$width <- width$width[i]
}

names(bcs) <- lenas

read_matrix <- function(sid) {
  Matrix::t(Seurat::Read10X_h5(paste(get_spatial_path(sid = sid), "/outs/raw_feature_bc_matrix.h5", sep = "")))
}

matrix <- map(.x = lenas, .f = read_matrix)
names(matrix) <- lenas
matrix[[1]]

umi_sum <- list() 

for (i in 1:length(lenas)) {
  umi_sum[[i]] <- data.frame(barcode =  row.names(matrix[[i]]),
                             sum_umi = Matrix::rowSums(matrix[[i]]))
  
}
names(umi_sum) <- lenas

umi_sum <- bind_rows(umi_sum, .id = "lena")
umi_sum


gene_sum <- list() 

for (i in 1:length(lenas)) {
  gene_sum[[i]] <- data.frame(barcode =  row.names(matrix[[i]]),
                             sum_gene = Matrix::rowSums(matrix[[i]] != 0))
  
}
names(gene_sum) <- lenas

gene_sum <- bind_rows(gene_sum, .id = "lena")
gene_sum

# If you need to look at the correlation of gene expression between samples
gene_umi_sum <- list() 

for (i in 1:length(lenas)) {
  gene_umi_sum[[i]] <- data.frame(gene =  colnames(matrix[[i]]),
                             gene_umi_sum = Matrix::colSums(matrix[[i]]))
  
}
names(gene_umi_sum) <- lenas

gene_umi_sum <- bind_rows(gene_umi_sum, .id = "lena")
gene_umi_sum

bcs_merge <- bind_rows(bcs, .id = "lena")
bcs_merge <- merge(bcs_merge,umi_sum, by = c("barcode", "lena"))
bcs_merge <- merge(bcs_merge,gene_sum, by = c("barcode", "lena"))

Define our color palette for plotting

myPalette <- colorRampPalette(rev(brewer.pal(11, "Spectral")))

plots <- list()

for (i in 1:length(lenas)) {

plots[[i]] <- bcs_merge %>% 
  filter(lena ==lenas[i]) %>% 
  filter(tissue =="1") %>% 
      ggplot(aes(x=imagecol_scaled,y=imagerow_scaled,fill=sum_umi)) +
                geom_spatial(data=images_tibble[i,], aes(grob=grob), x=0.5, y=0.5)+
                geom_point(shape = 21, colour = "black", size = 2, stroke = 0.1)+
                coord_cartesian(expand=FALSE)+
                scale_fill_gradientn(colours = myPalette(100))+
                #facet_wrap(~lena)+
                xlim(0,max(bcs_merge %>% 
                            filter(lena ==lenas[i]) %>% 
                            dplyr::select(width)))+
                ylim(max(bcs_merge %>% 
                            filter(lena ==lenas[i]) %>% 
                            dplyr::select(height)),0)+
                xlab("") +
                ylab("") +
                ggtitle(paste(lenas[i],": ", sample_type[i], sep = ""))+
                labs(fill = "UMI")+
                theme_set(theme_bw(base_size = 10))+
                theme(panel.grid.major = element_blank(), 
                        panel.grid.minor = element_blank(),
                        panel.background = element_blank(), 
                        axis.line = element_line(colour = "black"),
                        axis.text = element_blank())
}

patchwork::wrap_plots(plots)

plots <- list()

for (i in 1:length(lenas)) {

plots[[i]] <- bcs_merge %>% 
  filter(lena ==lenas[i]) %>% 
  filter(tissue =="1") %>% 
      ggplot(aes(x=imagecol_scaled,y=imagerow_scaled,fill=sum_gene)) +
                geom_spatial(data=images_tibble[i,], aes(grob=grob), x=0.5, y=0.5)+
                geom_point(shape = 21, colour = "black", size = 2, stroke = 0.1)+
                coord_cartesian(expand=FALSE)+
                scale_fill_gradientn(colours = myPalette(100))+
                #facet_wrap(~lena)+
                xlim(0,max(bcs_merge %>% 
                            filter(lena ==lenas[i]) %>% 
                            dplyr::select(width)))+
                ylim(max(bcs_merge %>% 
                            filter(lena ==lenas[i]) %>% 
                            dplyr::select(height)),0)+
                xlab("") +
                ylab("") +
                ggtitle(paste(lenas[i],": ", sample_type[i], sep = ""))+
                labs(fill = "Genes")+
                theme_set(theme_bw(base_size = 10))+
                theme(panel.grid.major = element_blank(), 
                        panel.grid.minor = element_blank(),
                        panel.background = element_blank(), 
                        axis.line = element_line(colour = "black"),
                        axis.text = element_blank())
}

patchwork::wrap_plots(plots)

plots <- list()

for (i in 1:length(lenas)) {

plots[[i]] <- bcs_merge %>% 
  filter(lena ==lenas[i]) %>%
  add_column(GAPDH = matrix[[i]][,"GAPDH"]) %>% 
      ggplot(aes(x=imagecol_scaled,y=imagerow_scaled,fill=GAPDH)) +
                geom_spatial(data=images_tibble[i,], aes(grob=grob), x=0.5, y=0.5)+
                geom_point(shape = 21, colour = "black", size = 2, stroke = 0.1)+
                coord_cartesian(expand=FALSE)+
                scale_fill_gradientn(colours = myPalette(100))+
                #facet_wrap(~lena)+
                xlim(0,max(bcs_merge %>% 
                            filter(lena ==lenas[i]) %>% 
                            dplyr::select(width)))+
                ylim(max(bcs_merge %>% 
                            filter(lena ==lenas[i]) %>% 
                            dplyr::select(height)),0)+
                xlab("") +
                ylab("") +
                ggtitle(paste(lenas[i],": ", sample_type[i], sep = ""))+
                labs(fill = "GAPDH UMI")+
                theme_set(theme_bw(base_size = 10))+
                theme(panel.grid.major = element_blank(), 
                        panel.grid.minor = element_blank(),
                        panel.background = element_blank(), 
                        axis.line = element_line(colour = "black"),
                        axis.text = element_blank())
}

patchwork::wrap_plots(plots)

plots <- list()

for (i in 1:length(lenas)) {

plots[[i]] <- bcs_merge %>% 
  filter(lena ==lenas[i]) %>%
  filter(tissue == "1") %>% 
  na.omit() %>% 
      ggplot(aes(x=imagecol_scaled,y=imagerow_scaled,fill=factor(Cluster))) +
                geom_spatial(data=images_tibble[i,], aes(grob=grob), x=0.5, y=0.5)+
                geom_point(shape = 21, colour = "black", size = 2, stroke = 0.1)+
                coord_cartesian(expand=FALSE)+
                scale_fill_manual(values = c("#b2df8a","#e41a1c","#377eb8","#4daf4a","#ff7f00","gold", 
                                             "#a65628", "#999999", "black", "white", "purple", "brown"))+
                xlim(0,max(bcs_merge %>% 
                            filter(lena ==lenas[i]) %>% 
                            dplyr::select(width)))+
                ylim(max(bcs_merge %>% 
                            filter(lena ==lenas[i]) %>% 
                            dplyr::select(height)),0)+
                xlab("") +
                ylab("") +
                ggtitle(paste(lenas[i],": ", sample_type[i], sep = ""))+
                labs(fill = "Cluster")+
                guides(fill = guide_legend(override.aes = list(size=3)))+
                theme_set(theme_bw(base_size = 10))+
                theme(panel.grid.major = element_blank(), 
                        panel.grid.minor = element_blank(),
                        panel.background = element_blank(), 
                        axis.line = element_line(colour = "black"),
                        axis.text = element_blank())
  
}
patchwork::wrap_plots(plots)

---

1. 10x Genomics, patrick.roelli@10xgenomics.com ↩︎

2. 10x Genomics, stephen.williams@10xgenomics.com↩︎

3. 10x Genomics, stephen.williams@10xgenomics.com↩︎